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"Kumar, Manish"
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2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.
Journal Article
Artificial Intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques
Annotation Build next-generation Artificial Intelligence systems with JavaKey FeaturesImplement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlibCreate self-learning systems using neural networks, NLP, and reinforcement learningBook DescriptionIn this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.What you will learnManage Artificial Intelligence techniques for big data with JavaBuild smart systems to analyze data for enhanced customer experienceLearn to use Artificial Intelligence frameworks for big dataUnderstand complex problems with algorithms and Neuro-Fuzzy systemsDesign stratagems to leverage data using Machine Learning processApply Deep Learning techniques to prepare data for modelingConstruct models that learn from data using open source toolsAnalyze big data problems using scalable Machine Learning algorithmsWho this book is forThis book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus
Book
Liquid crystals in photovoltaics: a new generation of organic photovoltaics
This article presents an overview of the developments in the field of organic photovoltaics (PVs) with liquid crystals (LCs). A brief introduction to the PV and LC fields is given first, followed by application of various LCs in organic PVs. Details of LCs used in bilayer solar cells, bulk heterojunction solar cells and dye-sensitized solar cells have been given. All the liquid crystalline materials used in PVs are structured and the efficiency of solar cells is tabulated. Finally, an outlook into the future of this newly emerging, fascinating and exciting field of self-organizing supramolecular LC PV research is provided.
Liquid crystals (LCs) have recently gained significant importance in organic photovoltaics (PVs). Power-conversion efficiency up to about 10% has reached in solar cells incorporating LCs. This review presents an overview of the developments in the field of organic PVs with LCs. Comprehensive details of LCs used in bilayer solar cells, bulk heterojunction solar cells and dye-sensitized solar cells have been given. An outlook into the future of this newly emerging, fascinating and exciting field of self-organizing supramolecular LC PV research is provided.
Journal Article
Anticancer potential of rhizome extract and a labdane diterpenoid from Curcuma mutabilis plant endemic to Western Ghats of India
Zingiberaceae plants are well known for their use in ethnomedicine.
Curcuma mutabilis
Škorničk., M. Sabu & Prasanthk., is an endemic Zingiberaceae species from Western Ghats of Kerala, India. Here, we report for the first time, the anticancer potential of petroleum ether extract from
C. mutabilis
rhizome (CMRP) and a novel labdane diterpenoid, (
E
)-14, 15-epoxylabda-8(17), 12-dien-16-al (Cm epoxide) isolated from it. CMRP was found to be a mixture of potent bioactive compounds including Cm epoxide. Both the extract and the compound displayed superior antiproliferative activity against several human cancer cell lines, without any display of cytotoxicity towards normal human cells such as peripheral blood derived lymphocytes and erythrocytes. CMRP treatment resulted in phosphatidylserine externalization, increase in the levels of intracellular ROS, Ca
2+
, loss of mitochondrial membrane potential as well as fragmentation of genomic DNA. Analyses of transcript profiling and immunostained western blots of extract-treated cancer cells confirmed induction of apoptosis by both intrinsic and extrinsic pathways. The purified compound, Cm epoxide, was also found to induce apoptosis in many human cancer cell types tested. Both CMRP and the Cm epoxide were found to be pharmacologically safe in terms of acute toxicity assessment using Swiss albino mice model. Further, molecular docking interactions of Cm epoxide with selected proteins involved in cell survival and death were also indicative of its druggability. Overall, our findings reveal that the endemic
C. mutabilis
rhizome extract and the compound Cm epoxide isolated from it are potential candidates for development of future cancer chemotherapeutics.
Journal Article
Species richness, phylogenetic diversity and phylogenetic structure patterns of exotic and native plants along an elevational gradient in the Himalaya
BackgroundSo far, macroecological studies in the Himalaya have mostly concentrated on spatial variation of overall species richness along the elevational gradient. Very few studies have attempted to document the difference in elevational richness patterns of native and exotic species. In this study, this knowledge gap is addressed by integrating data on phylogeny and elevational distribution of species to identify the variation in species richness, phylogenetic diversity and phylogenetic structure of exotic and native plant species along an elevational gradient in the Himalaya.ResultsSpecies distribution patterns for exotic and native species differed; exotics tended to show maximum species richness at low elevations while natives tended to predominate at mid-elevations. Native species assemblages showed higher phylogenetic diversity than the exotic species assemblages over the entire elevational gradient in the Himalaya. In terms of phylogenetic structure, exotic species assemblages showed majorly phylogenetic clustering while native species assemblages were characterized by phylogenetic overdispersion over the entire gradient.ConclusionsThe findings of this study indicate that areas with high native species richness and phylogenetic diversity are less receptive to exotic species and vice versa in the Himalaya. Species assemblages with high native phylogenetic overdispersion are less receptive to exotic species than the phylogenetically clustered assemblages. Different ecological processes (ecological filtering in case of exotics and resource and niche competition in case of natives) may govern the distribution of exotic and native species along the elevational gradient in the Himalaya.
Journal Article
Octonion quadratic-phase Fourier transform: inequalities, uncertainty principles, and examples
In this article, we define the octonion quadratic-phase Fourier transform (OQPFT) and derive its inversion formula, including its fundamental properties such as linearity, parity, modulation, and shifting. We also establish its relationship with the quaternion quadratic-phase Fourier transform (QQPFT). Further, we derive the Parseval formula and the Riemann–Lebesgue lemma using this transform. Furthermore, we formulate two important inequalities (sharp Pitt’s and sharp Hausdorff–Young’s inequalities) and three main uncertainty principles (logarithmic, Donoho–Stark’s, and Heisenberg’s uncertainty principles) for the OQPFT. To complete our investigation, we construct three elementary examples of signal theory with graphical interpretations to illustrate the use of OQPFT and discuss their particular cases.
Journal Article
Artificial Intelligence for Big Data
Build next-generation Artificial Intelligence systems with Java Key Features * Implement AI techniques to build smart applications using Deeplearning4j * Perform big data analytics to derive quality insights using Spark MLlib * Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn * Manage Artificial Intelligence techniques for big data with Java * Build smart systems to analyze data for enhanced customer experience * Learn to use Artificial Intelligence frameworks for big data * Understand complex problems with algorithms and Neuro-Fuzzy systems * Design stratagems to leverage data using Machine Learning process * Apply Deep Learning techniques to prepare data for modeling * Construct models that learn from data using open source tools * Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.
eBook
Transforming air pollution management in India with AI and machine learning technologies
A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM
2.5
concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM
2.5
pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM
2.5
concentrations across India. The results reveal its exceptional precision in PM
2.5
prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m
3
. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM
2.5
concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.
Journal Article